{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from models.disnce import DisNCELoss\n",
    "import cv2\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "175\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "data_path = './results/cutrain_MidCity_RH/test_300/images/'\n",
    "img_list = os.listdir(data_path+'pred_B')\n",
    "print(len(img_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['--dataroot ../middle_cityRH --model cut --name exper']\n",
      "----------------- Options ---------------\n",
      "                 CUT_mode: CUT                           \n",
      "               batch_size: 1                             \n",
      "                    beta1: 0.5                           \n",
      "                    beta2: 0.999                         \n",
      "          checkpoints_dir: ./checkpoints                 \n",
      "           continue_train: False                         \n",
      "                crop_size: 256                           \n",
      "                 dataroot: placeholder                   \n",
      "             dataset_mode: unaligned                     \n",
      "                direction: AtoB                          \n",
      "              display_env: main                          \n",
      "             display_freq: 400                           \n",
      "               display_id: -1                            \n",
      "            display_ncols: 4                             \n",
      "             display_port: 8097                          \n",
      "           display_server: http://localhost              \n",
      "          display_winsize: 256                           \n",
      "               easy_label: experiment_name               \n",
      "                    epoch: latest                        \n",
      "              epoch_count: 1                             \n",
      "          evaluation_freq: 5000                          \n",
      "        flip_equivariance: False                         \n",
      "                 gan_mode: lsgan                         \n",
      "                  gpu_ids: 0                             \n",
      "                init_gain: 0.02                          \n",
      "                init_type: xavier                        \n",
      "                 input_nc: 3                             \n",
      "                  isTrain: True                          \t[default: None]\n",
      "            lambda_DisNCE: 1.0                           \n",
      "               lambda_GAN: 1.0                           \n",
      "               lambda_NCE: 1.0                           \n",
      "                load_size: 286                           \n",
      "                       lr: 0.0002                        \n",
      "           lr_decay_iters: 50                            \n",
      "                lr_policy: linear                        \n",
      "         max_dataset_size: inf                           \n",
      "                    model: cutrain                       \n",
      "                 n_epochs: 200                           \n",
      "           n_epochs_decay: 200                           \n",
      "               n_layers_D: 3                             \n",
      "                     name: experiment_name               \n",
      "                    nce_T: 0.07                          \n",
      "                  nce_idt: True                          \n",
      "nce_includes_all_negatives_from_minibatch: False                         \n",
      "               nce_layers: 0,4,8,12,16                   \n",
      "                      ndf: 64                            \n",
      "                     netD: basic                         \n",
      "                     netF: mlp_sample                    \n",
      "                  netF_nc: 256                           \n",
      "                     netG: resnet_9blocks                \n",
      "                      ngf: 64                            \n",
      "             no_antialias: False                         \n",
      "          no_antialias_up: False                         \n",
      "               no_dropout: True                          \n",
      "                  no_flip: False                         \n",
      "                  no_html: False                         \n",
      "                    normD: instance                      \n",
      "                    normG: instance                      \n",
      "              num_patches: 10                            \n",
      "              num_threads: 4                             \n",
      "                output_nc: 3                             \n",
      "                    phase: train                         \n",
      "                pool_size: 0                             \n",
      "               preprocess: crop                          \n",
      "          pretrained_name: None                          \n",
      "               print_freq: 100                           \n",
      "         random_scale_max: 3.0                           \n",
      "             save_by_iter: False                         \n",
      "          save_epoch_freq: 50                            \n",
      "         save_latest_freq: 5000                          \n",
      "           serial_batches: False                         \n",
      "stylegan2_G_num_downsampling: 1                             \n",
      "                   suffix:                               \n",
      "         update_html_freq: 1000                          \n",
      "                  verbose: False                         \n",
      "----------------- End -------------------\n",
      "device: cuda:0\n"
     ]
    }
   ],
   "source": [
    "# set the environment\n",
    "import models.networks as networks\n",
    "from options.test_options import TestOptions\n",
    "from options.train_options import TrainOptions\n",
    "from sys import argv\n",
    "argv.clear()\n",
    "argv.append('--dataroot ../middle_cityRH --model cut --name exper')\n",
    "print(argv)\n",
    "opt = TrainOptions().parse()\n",
    "device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if opt.gpu_ids else torch.device('cpu')\n",
    "print('device:',device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image size: (600, 300)\n",
      "tensor size: torch.Size([1, 3, 512, 512])\n"
     ]
    }
   ],
   "source": [
    "# prepare data\n",
    "from PIL import Image\n",
    "example_name = img_list[4]\n",
    "exper_obj_names = ['pred_B', 'pred_R', 'O', 'B']\n",
    "obj_num = {}\n",
    "obj_tensor = {}\n",
    "\n",
    "transform_list = transforms.Compose([\n",
    "    transforms.CenterCrop(512),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "for name in exper_obj_names:\n",
    "#     obj_num[name] = cv2.imread(os.path.join(data_path,name,example_name))\n",
    "    obj_num[name] = Image.open(os.path.join(data_path,name,example_name)).convert('RGB')\n",
    "    obj_tensor[name] = transform_list(obj_num[name]).unsqueeze(0)\n",
    "print('image size:', obj_num['pred_B'].size)\n",
    "print('tensor size:',obj_tensor['pred_B'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "netF = 0\n",
    "netG = 0\n",
    "netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.normG, not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, opt.no_antialias_up, opt.gpu_ids, opt)\n",
    "netF = networks.define_F(opt.input_nc, opt.netF, opt.normG, not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, opt.gpu_ids, opt)\n",
    "# dict = torch.load('./checkpoints/MidCity_RH/300_net_F.pth')\n",
    "netG.load_state_dict(torch.load('./checkpoints/MidCity_RH/300_net_G.pth'))\n",
    "# netF.load_state_dict(torch.load('./checkpoints/MidCity_RH/300_net_F.pth'))\n",
    "# print(netF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "cuda:0\n",
      "cuda:0\n",
      "cuda:0\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "print(device)\n",
    "for obj in obj_tensor.keys():\n",
    "    obj_tensor[obj] = obj_tensor[obj].to(device)\n",
    "    print(obj_tensor[obj].device)\n",
    "numpatches = opt.num_patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from util.util import tensor2im\n",
    "from matplotlib import pyplot as plt\n",
    "pred_B_num = tensor2im(obj_tensor['pred_B'])\n",
    "pred_R_num = tensor2im(obj_tensor['pred_R'])\n",
    "print(pred_B_num.shape)\n",
    "plt.imshow(pred_B_num)\n",
    "plt.show()\n",
    "plt.imshow(pred_R_num)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_sample: torch.Size([256, 3])\n",
      "torch.Size([1, 3, 300, 600])\n",
      "torch.Size([256, 256])\n",
      "x_sample: torch.Size([256, 3])\n",
      "x_sample: torch.Size([256, 3])\n",
      "x_sample: torch.Size([256, 3])\n",
      "pred_B - pred_R: tensor(9.5892, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "GT_B - pred_R: tensor(9.1068, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "Smaller loss should be better: intra image relative while inter imgae not relative\n"
     ]
    }
   ],
   "source": [
    "# image_level mutual loss test\n",
    "feat_pred_B, ids = netF([obj_tensor['pred_B']], numpatches, None)\n",
    "print(obj_tensor['pred_B'].shape)\n",
    "print(feat_pred_B[0].shape)\n",
    "feat_pred_R, _ = netF([obj_tensor['pred_R']], numpatches, ids)\n",
    "feat_B, _ = netF([obj_tensor['B']], numpatches, ids)\n",
    "feat_O, _ = netF([obj_tensor['O']], numpatches, ids)\n",
    "\n",
    "feat_pred_B = feat_pred_B[0]\n",
    "feat_pred_R = feat_pred_R[0]\n",
    "feat_B = feat_B[0]\n",
    "feat_O = feat_O[0]\n",
    "criterDisNCE = DisNCELoss(opt).to(device)\n",
    "print('pred_B - pred_R:',criterDisNCE(feat_pred_B,feat_pred_R))\n",
    "print('GT_B - pred_R:',criterDisNCE(feat_B,feat_pred_R))\n",
    "print('Smaller loss should be better: intra image relative while inter imgae not relative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_sample: torch.Size([256, 3])\n",
      "x_sample: torch.Size([256, 128])\n",
      "x_sample: torch.Size([256, 256])\n",
      "x_sample: torch.Size([256, 256])\n",
      "x_sample: torch.Size([256, 256])\n",
      "5\n",
      "x_sample: torch.Size([256, 3])\n",
      "x_sample: torch.Size([256, 128])\n",
      "x_sample: torch.Size([256, 256])\n",
      "x_sample: torch.Size([256, 256])\n",
      "x_sample: torch.Size([256, 256])\n",
      "x_sample: torch.Size([256, 3])\n",
      "x_sample: torch.Size([256, 128])\n",
      "x_sample: torch.Size([256, 256])\n",
      "x_sample: torch.Size([256, 256])\n",
      "x_sample: torch.Size([256, 256])\n",
      "tensor(8.4980, device='cuda:0', grad_fn=<DivBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# feature_level\n",
    "# extract feature\n",
    "nce_layers = [0,4,8,12,16]\n",
    "feat_pred_B = netG(obj_tensor['pred_B'], nce_layers, encode_only=True)\n",
    "feat_pred_R = netG(obj_tensor['pred_R'], nce_layers, encode_only=True)\n",
    "feat_B = netG(obj_tensor['B'], nce_layers, encode_only=True)\n",
    "\n",
    "# sampling\n",
    "feat_pred_B_pool, ids = netF(feat_pred_B, numpatches, None)\n",
    "print(len(ids))\n",
    "feat_pred_R_pool, _ = netF(feat_pred_R, numpatches, ids)\n",
    "feat_B_pool, _ = netF(feat_B, numpatches, ids)\n",
    "\n",
    "total_nce_loss = 0\n",
    "criterDisNCE = DisNCELoss(opt).to(device)\n",
    "for f_b, f_r, nce_layer in zip(feat_pred_B_pool, feat_pred_R_pool, nce_layers):\n",
    "    loss = criterDisNCE(f_b, f_r)\n",
    "    total_nce_loss += loss.mean()\n",
    "print(total_nce_loss/len(nce_layers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B_R/PB_R: tensor(41.1155, device='cuda:0') tensor(40.9560, device='cuda:0')\n",
      "B_R/PB_R: tensor(40.9270, device='cuda:0') tensor(40.7788, device='cuda:0')\n",
      "B_R/PB_R: tensor(40.9811, device='cuda:0') tensor(40.8589, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.1758, device='cuda:0') tensor(41.0651, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.1253, device='cuda:0') tensor(40.9704, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.1409, device='cuda:0') tensor(41.0219, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.1194, device='cuda:0') tensor(41.0737, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.0661, device='cuda:0') tensor(41.0631, device='cuda:0')\n",
      "B_R/PB_R: tensor(40.9984, device='cuda:0') tensor(40.9513, device='cuda:0')\n",
      "B_R/PB_R: tensor(40.9787, device='cuda:0') tensor(40.9101, device='cuda:0')\n",
      "B_R/PB_R: tensor(40.9471, device='cuda:0') tensor(40.9227, device='cuda:0')\n",
      "B_R/PB_R: tensor(40.9754, device='cuda:0') tensor(40.9317, device='cuda:0')\n",
      "B_R/PB_R: tensor(40.9718, device='cuda:0') tensor(40.9516, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.0128, device='cuda:0') tensor(41.0082, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.0245, device='cuda:0') tensor(41.0044, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.0362, device='cuda:0') tensor(41.0155, device='cuda:0')\n",
      "B_R/PB_R: tensor(41.0369, device='cuda:0') tensor(41.0300, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# Analysis All\n",
    "exper_obj_names = ['pred_B', 'pred_R', 'O', 'B']\n",
    "obj_tensor = {}\n",
    "transform_list = transforms.Compose([transforms.ToTensor(),\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "nce_layers = [0,4,8,12,16]\n",
    "criterDisNCE = DisNCELoss(opt).to(device)\n",
    "aver_nce_loss_B_R = 0\n",
    "aver_nce_loss_PB_R = 0\n",
    "count = 0\n",
    "for example_name in img_list:\n",
    "    for name in exper_obj_names:\n",
    "        img = cv2.imread(os.path.join(data_path,name,example_name))\n",
    "        obj_tensor[name] = transform_list(img).unsqueeze(0).to(device)\n",
    "    # feature extract\n",
    "    with torch.no_grad():\n",
    "        feat_pred_B = netG(obj_tensor['pred_B'], nce_layers, encode_only=True)\n",
    "        feat_pred_R = netG(obj_tensor['pred_R'], nce_layers, encode_only=True)\n",
    "        feat_B = netG(obj_tensor['B'], nce_layers, encode_only=True)\n",
    "        # sampling\n",
    "        feat_pred_B_pool, ids = netF(feat_pred_B, numpatches, None)\n",
    "        feat_pred_R_pool, _ = netF(feat_pred_R, numpatches, ids)\n",
    "        feat_B_pool, _ = netF(feat_B, numpatches, ids)\n",
    "        tmp_nce_loss_B_R = 0\n",
    "        tmp_nce_loss_PB_R = 0\n",
    "        for f_b, f_pb, f_r, nce_layer in zip(feat_B_pool, feat_pred_B_pool, feat_pred_R_pool, nce_layers):\n",
    "            loss_B_R = criterDisNCE(f_b, f_r)\n",
    "            loss_PB_R = criterDisNCE(f_pb, f_r)\n",
    "            tmp_nce_loss_B_R += loss_B_R.mean()\n",
    "            tmp_nce_loss_PB_R += loss_PB_R.mean()\n",
    "        aver_nce_loss_B_R = (aver_nce_loss_B_R*count + tmp_nce_loss_B_R)/(count+1)\n",
    "        aver_nce_loss_PB_R = (aver_nce_loss_PB_R*count + tmp_nce_loss_PB_R)/(count+1)\n",
    "        count += 1\n",
    "        if (count%10 == 0):\n",
    "            print('B_R/PB_R:',aver_nce_loss_B_R, aver_nce_loss_PB_R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feat_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test netF inside\n",
    "num_patches = 256\n",
    "feat = obj_tensor['pred_B']\n",
    "print(feat.shape)\n",
    "B, H, W = feat.shape[0], feat.shape[2], feat.shape[3]\n",
    "feat_reshape = feat.permute(0, 2, 3, 1).flatten(1, 2)\n",
    "print(feat_reshape.shape)\n",
    "\n",
    "patch_id = torch.randperm(feat_reshape.shape[1], device=device)\n",
    "print(patch_id.shape)\n",
    "patch_id = patch_id[:int(min(num_patches, patch_id.shape[0]))]\n",
    "print(patch_id.shape)\n",
    "\n",
    "x_sample = feat_reshape[:, patch_id, :].flatten(0,1)\n",
    "# x_sample = feat_reshape[:, patch_id, :]\n",
    "print(x_sample.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# new NetF test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 512, 512, 3])\n",
      "torch.Size([1, 10, 16, 16, 3])\n"
     ]
    }
   ],
   "source": [
    "patch_h = 16\n",
    "patch_w = 16\n",
    "# image_level\n",
    "feat = obj_tensor['pred_B']\n",
    "# print(feat.shape)\n",
    "B, C, H, W = feat.shape\n",
    "if H != W:\n",
    "    raise ValueError('Need Square Input')\n",
    "feat_reshape = feat.permute(0, 2, 3, 1)\n",
    "feat_samples = torch.zeros([1,opt.num_patches,patch_h,patch_w,C], device = feat.device)\n",
    "print(feat_reshape.shape)\n",
    "print(feat_samples.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "patch_id_h = torch.randperm(feat_reshape.shape[1]-patch_h-1, device=device)[0:opt.num_patches]\n",
    "patch_id_w = torch.randperm(feat_reshape.shape[2]-patch_w-1, device=device)[0:opt.num_patches]\n",
    "pacth_id = [patch_id_h, patch_id_w]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patch_id_h.device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 256, 768])\n"
     ]
    }
   ],
   "source": [
    "# feat_samples[:,i,:,:,:] = \n",
    "for i in range(0,opt.num_patches):\n",
    "    feat_samples[:,i,:,:,:] = feat_reshape[:, patch_id_h[i]:patch_id_h[i]+patch_h, patch_id_w[i]:patch_id_w[i]+patch_w, :]\n",
    "feat_samples = feat_samples.flatten(2,4)\n",
    "print(feat_samples.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "class RealPatchSample(nn.Module):\n",
    "    def __init__(self, use_mlp=False, init_type='normal', init_gain=0.02, nc=256, gpu_ids=[]):\n",
    "        # potential issues: currently, we use the same patch_ids for multiple images in the batch\n",
    "        super(RealPatchSample, self).__init__()\n",
    "        self.l2norm = Normalize(2)\n",
    "        self.use_mlp = use_mlp\n",
    "        self.nc = nc  # hard-coded\n",
    "        self.mlp_init = False\n",
    "        self.init_type = init_type\n",
    "        self.init_gain = init_gain\n",
    "        self.gpu_ids = gpu_ids\n",
    "        self.patch_h = 16\n",
    "        self.patch_w = 16\n",
    "\n",
    "    def create_mlp(self, feats):\n",
    "        for mlp_id, feat in enumerate(feats):\n",
    "            input_nc = feat.shape[1]\n",
    "            mlp = nn.Sequential(*[nn.Linear(input_nc, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)])\n",
    "            if len(self.gpu_ids) > 0:\n",
    "                mlp.cuda()\n",
    "            setattr(self, 'mlp_%d' % mlp_id, mlp)\n",
    "        init_net(self, self.init_type, self.init_gain, self.gpu_ids)\n",
    "        self.mlp_init = True\n",
    "        \n",
    "    def forward(self, feats, num_patches=64, patch_ids=None):\n",
    "        return_ids = []\n",
    "        return_feats = []\n",
    "        if self.use_mlp and not self.mlp_init:\n",
    "            self.create_mlp(feats)\n",
    "        for feat_id, feat in enumerate(feats):\n",
    "            B, C, H, W = feat.shape\n",
    "            feat_reshape = feat.permute(0, 2, 3, 1) #(B, H, W, C)\n",
    "            feat_samples = torch.zeros([feat.shape[0],num_patches,self.patch_h,self.patch_w,C],device=feat.device)\n",
    "            if num_patches > 0:\n",
    "                if patch_ids is not None:\n",
    "                    patch_id_h = patch_ids[feat_id][0]\n",
    "                    patch_id_w = patch_ids[feat_id][1]\n",
    "                else:\n",
    "                    patch_id_h = torch.randperm(feat_reshape.shape[1]-self.patch_h-1, device=feat.device)[0:num_patches]\n",
    "                    patch_id_w = torch.randperm(feat_reshape.shape[2]-self.patch_w-1, device=feat.device)[0:num_patches]\n",
    "                # patch_id shape: 256\n",
    "                for i in range(0,num_patches):\n",
    "                    feat_samples[:,i,:,:,:] = feat_reshape[:, patch_id_h[i]:patch_id_h[i]+self.patch_h, patch_id_w[i]:patch_id_w[i]+self.patch_w, :]\n",
    "                feat_samples = feat_samples.flatten(2,4)\n",
    "                feat_samples = feat_samples.flatten(0,1)\n",
    "            else:\n",
    "                raise ValueError('num_patches should be positive')\n",
    "            if self.use_mlp:\n",
    "                mlp = getattr(self, 'mlp_%d' % feat_id)\n",
    "                feat_samples = mlp(feat_samples)\n",
    "            return_ids.append([patch_id_h, patch_id_w])\n",
    "            feat_samples = self.l2norm(feat_samples)\n",
    "\n",
    "            return_feats.append(feat_samples)\n",
    "        return return_feats, return_ids\n",
    "\n",
    "    \n",
    "class Normalize(nn.Module):\n",
    "\n",
    "    def __init__(self, power=2):\n",
    "        super(Normalize, self).__init__()\n",
    "        self.power = power\n",
    "\n",
    "    def forward(self, x):\n",
    "        norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)\n",
    "        out = x.div(norm + 1e-7)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 768])\n",
      "torch.Size([10, 768])\n",
      "1 10\n",
      "pred_B - pred_R: tensor(8.6080)\n",
      "GT_B - pred_R: tensor(8.2332)\n",
      "Smaller loss should be better: intra image relative while inter imgae not relative\n"
     ]
    }
   ],
   "source": [
    "netF = RealPatchSample()\n",
    "# image_level mutual loss test\n",
    "numpatches = 10\n",
    "feat_pred_B, ids = netF([obj_tensor['pred_B']], numpatches, None)\n",
    "feat_pred_R, _ = netF([obj_tensor['pred_R']], numpatches, ids)\n",
    "feat_B, _ = netF([obj_tensor['B']], numpatches, ids)\n",
    "feat_O, _ = netF([obj_tensor['O']], numpatches, ids)\n",
    "\n",
    "feat_pred_B = feat_pred_B[0]\n",
    "feat_pred_R = feat_pred_R[0]\n",
    "feat_B = feat_B[0]\n",
    "feat_O = feat_O[0]\n",
    "\n",
    "print(feat_pred_B.shape)\n",
    "print(feat_pred_R.shape)\n",
    "print(opt.batch_size, opt.num_patches)\n",
    "criterDisNCE = DisNCELoss(opt).to(device)\n",
    "print('pred_B - pred_R:',criterDisNCE(feat_pred_B,feat_pred_R))\n",
    "print('GT_B - pred_R:',criterDisNCE(feat_B,feat_pred_R))\n",
    "print('Smaller loss should be better: intra image relative while inter imgae not relative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B_R/PB_R: tensor(11.9665, device='cuda:0') tensor(11.6241, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.6198, device='cuda:0') tensor(11.3496, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5892, device='cuda:0') tensor(11.3257, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5909, device='cuda:0') tensor(11.3245, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5888, device='cuda:0') tensor(11.2772, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5524, device='cuda:0') tensor(11.2773, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5522, device='cuda:0') tensor(11.3467, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5264, device='cuda:0') tensor(11.3814, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5492, device='cuda:0') tensor(11.3613, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5509, device='cuda:0') tensor(11.3322, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5215, device='cuda:0') tensor(11.3441, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5768, device='cuda:0') tensor(11.3750, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5788, device='cuda:0') tensor(11.3987, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5668, device='cuda:0') tensor(11.4131, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.6170, device='cuda:0') tensor(11.4438, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.6196, device='cuda:0') tensor(11.4484, device='cuda:0')\n",
      "B_R/PB_R: tensor(11.5830, device='cuda:0') tensor(11.4215, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# Analysis All\n",
    "exper_obj_names = ['pred_B', 'pred_R', 'O', 'B']\n",
    "obj_tensor = {}\n",
    "transform_list = transforms.Compose([\n",
    "    transforms.CenterCrop(512),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "criterDisNCE = DisNCELoss(opt).to(device)\n",
    "aver_nce_loss_B_R = 0\n",
    "aver_nce_loss_PB_R = 0\n",
    "count = 0\n",
    "for example_name in img_list:\n",
    "    for name in exper_obj_names:\n",
    "        img = Image.open(os.path.join(data_path,name,example_name)).convert('RGB')\n",
    "        obj_tensor[name] = transform_list(img).unsqueeze(0).to(device)\n",
    "    # feature extract\n",
    "    with torch.no_grad():\n",
    "        feat_pred_B = obj_tensor['pred_B']\n",
    "        feat_pred_R = obj_tensor['pred_R']\n",
    "        feat_B = obj_tensor['B']\n",
    "        # sampling\n",
    "        feat_pred_B_pool, ids = netF(feat_pred_B, numpatches, None)\n",
    "        ids = ids[0]\n",
    "        feat_pred_R_pool, _ = netF(feat_pred_R, numpatches, ids)\n",
    "        feat_B_pool, _ = netF(feat_B, numpatches, ids)\n",
    "        tmp_nce_loss_B_R = 0\n",
    "        tmp_nce_loss_PB_R = 0\n",
    "        for f_b, f_pb, f_r in zip(feat_B_pool, feat_pred_B_pool, feat_pred_R_pool):\n",
    "            loss_B_R = criterDisNCE(f_b, f_r)\n",
    "            loss_PB_R = criterDisNCE(f_pb, f_r)\n",
    "            tmp_nce_loss_B_R += loss_B_R.mean()\n",
    "            tmp_nce_loss_PB_R += loss_PB_R.mean()\n",
    "        aver_nce_loss_B_R = (aver_nce_loss_B_R*count + tmp_nce_loss_B_R)/(count+1)\n",
    "        aver_nce_loss_PB_R = (aver_nce_loss_PB_R*count + tmp_nce_loss_PB_R)/(count+1)\n",
    "        count += 1\n",
    "        if (count%10 == 0):\n",
    "            print('B_R/PB_R:',aver_nce_loss_B_R, aver_nce_loss_PB_R)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
